About Me

Dr. Armstrong is a researcher at the intersection of software ecosystem sustainability, affective computing, the trustworthiness of safety-critical systems, and software engineering for machine learning applications, including foundational models, AIWare, and Agentware. He mines massive datasets, including software repositories, and applies socio-technical data science techniques to uncover patterns and empirically make informed decisions.

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Interests
  • Affective Computing
  • Artificial Intelligence
  • Data Science
  • AI Trusthworhiness AI
  • Safety-critical systems
  • SECO Sustaianbility
  • Carbon foorprint/ Climate change
Education
  • PhD SECO Sustaianbility

    Queens University

  • M.A.Sc Data Science

    Polytechnique Montreal

📚 My Research

I am a research scientist at the DEEL project, Polytechnique Montreal. I use mixed-methods research across diverse use cases to explore the Why, How, and What of ecosystem sustainability and the trustworthiness of safety-critical AI systems.

— Please reach out to collaborate 😃

Featured Publications
Recent Publications
(2024). An empirical study of testing machine learning in the wild. ACM Transactions on Software Engineering and Methodology.
(2024). Deep learning model reuse in the huggingface community: Challenges, benefit and trends. IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER).
(2023). A Grounded Theory of Cross-community SECOs: Feedback Diversity vs. Synchronization. IEEE Transactions on Software Engineering.
(2023). Studying the Practices of Testing Machine Learning Software in the Wild. arXiv preprint arXiv:2312.12604.
(2022). A mixed-methods analysis of micro-collaborative coding practices in OpenStack. Empirical Software Engineering.
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